Start End Name Affiliation Talk Title time time 09:00 9:30 Registration 9:30 9:45 Michael O'Keefe LPS Welcome & Introduction 1 9:45 10:15 Adam Moss Independent An introduction to petrophysical rock typing 2 10:15 10:45 Romain Reboul CGG Petrophysical rock typing at two scales Elementary Knowledge – Spectroscopy Logging 3 10:45 11:15 Tom Bradley – what is it and how can we use it? 11:15 11:45 Break Jonathan Hall, Hyres Geoscience Carbonate rock fabric and its relationship to 4 11:45 12:15 Dr Benoit Vincent, Solutions, Cambridge storage & flow Dr Pete Gutteridge Carbonates Facies based rock typing in thinly bedded 5 12:15 12:45 Dawn Houliston BP turbidites 12:45 13:45 Lunch 6 13:45 14:15 Hendrik Rohler OMV Rock typing for relative permeability Sedimentological analysis and permeability Task Fronterra prediction within a heterogeneous carbonate 7 14:15 14:45 Giancarlo Rizzi Geoscience reservoir using borehole images and historical dipmeters Rock type based poroperm & continuous 8 14:45 15:15 Samuel Ojo University of Leeds permeability 15:15 15:45 Break Influence of formation factors on wettability 9 15:45 16:15 Federica Raimondi Imperial from 1D NMR Evaluation of variable oil volume and rock 10 16:15 16:45 Carole Reynaud types with NMR and dielectric dispersion logs in a carbonate reservoir high deviation drain 16:45 17:00 Michael O'Keefe LPS Closing Comments 17:00 onwards Refreshments@ The King's Head {Presidents’ Evening}

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The following pages contain the Abstracts

An introduction to petrophysical rock typing Adam Moss Independent

This talk aims to define the concept of ‘Petrophysical Rock Typing’ and set the scene for the day’s seminar. Literature on the topic will be reviewed. Petrophysical rock types will be compared and contrasted with geological rock type groupings. Previous workers use of core and log data to classify petrophysical rock type schemes will be discussed, as well as the applicability of each method to common formation evaluation workflows. The issues of scale of measurement and heterogeneity will be addressed and their effect on the classification and prediction of petrophysical rock types will be examined. The talk will present example of the use of petrophysical rock types from clastics, carbonates and tight gas reservoirs.

Petrophysical rock typing at two scales Romain Reboul CGG

Rock typing is a field of that has become more developed hence more used. This exponential development is helped by the progress in statistical / probabilistic tools like neural networks. Neural networks decrease the cost and time of rock typing studies while increasing the output possibilities.

The first scale of rock typing is the reservoir scale or development scale. It always had been a key step in the reservoir modelling, used to define electrofacies with distinct petrophysical behaviour from raw wireline log and core data. The electrofacies (logs having similar responses) turns into rock types when associated with specifics petrophysical properties and / or geological features. The rock types created are then used as a vector to fill the reservoir model with petrophysical properties such as poro/perm and saturation height function with detailed . This rock typing is using the fine geological scale (core data) to capture the maximum of heterogeneities and use the logs scale to propagate them at reservoir scale.

Figure 1: Reservoir Rock types - 2 cored wells and one un-cored well

The second scale of rock typing is the field scale or discovery scale. This has been less developed than the first one but the new tools un-lock the possibility of dealing with multivariable dimension and large volumes of data in a small amount of time. As with the first scale, rock typing, is used to define electrofacies, with distinct petrophysical properties and geological features (if core coverage allows it). It adds the distinct “elastic” log responses (Sonic and density). Hence is used to link the petrophysical properties K-Phi, lithology, (broad geological information), to geophysical measurement.

The elastic properties, computed by seismic inversion techniques, are thus connected to and calibrated by, the rock types at the wells which include the elastic logs in their creation.

Thus Rock Typing can be used either in creating reservoir models or in obtaining a better understanding of the seismic data in terms of geology and properties. The key is the use of logs to connect the disparate scales together.

Figure 2: Electrofacies at field scale (Jurassic formations) linked to seismic

Elementary Knowledge – Spectroscopy Logging – what is it and how can we use it?

Tom Bradley Baker Hughes

In recent years elemental spectroscopy logging has become an accepted technique for accurate quantification of formation elemental composition. And with the appropriate analysis techniques these elemental compositions can be used to determine formation mineralogy and other formation properties. In this presentation we discuss the theory of the measurement; the evolution of the tools from the early chemical source tools to modern electrical source tools; considerations in data acquisition; and application of the data to determine formation elemental composition. We then discuss how the elemental data is used to determine formation mineralogy and the further applications such as confident determination of , fluid identification and saturation.

Carbonate rock fabric and its relationship to storage & flow Jonathan Hall, Dr Benoit Vincent, Dr Pete Gutteridge Hyres Geoscience Solutions, Cambridge Carbonates

When petrophysical rock typing carbonate reservoirs, the focus should be upon rock fabric and its relationship to storage and flow capacity. For many Middle East and Central Americas giant carbonate reservoirs, rock quality is governed by the proportion of matrix lime mud to allochemical fossiliferous debris, pellets, ooids and intraclasts.

Partitioning porous dolostone from limestone is important but conventional log measurements do not serve to distinguish the textural nuances that give rise to wide variations in permeability for a given porosity in mono-mineralic rock. Hall et al. (1996) and Ye et al. (1997, 1998) demonstrated twenty years ago how texture extraction from borehole image data can be used to complement conventional logs for carbonate rock typing. These works illustrated that the representative elementary volume, (REV), for many carbonate rock types is poorly characterised at scales of conventional log and core analysis, and that iterative multi scale solutions are needed.

The relationship between storage and flow capacity can be described by just three Flow Zone Indicator families for this exclusively calcitic Middle East Lower Cretaceous supergiant accumulation.

More recently, petrophysical characterisation of carbonates has adopted an experimental design approach to describing rock and pore fabric; iteratively reducing uncertainty. ‘Texture sensitive’ models have been used (Ramakrishnan et al. 2001, Ramamoorthy et al. 2008). We illustrate how some macroscopic descriptions of effective media parameters; e.g. ‘cementation exponent’, have been estimated using effective medium approximations derived from complementary, acoustic,

measurements and, thereafter, using methods of seismic inversion, drilling prospects can be high graded.

The future of carbonate reservoir rock typing requires the development of fully coupled global optimisation routines that exploit available effective medium models. Further, a better pore classification system is required, as existing schemes for carbonate rock typing may capture sedimentology and diagenetic processes but ignore intrinsic dynamic flow-related properties. As Arve Lonoy expresses it, “In many carbonate reservoirs, it is, therefore, difficult to generate predictive models for reservoir-quality distribution.”

Facies based rock typing in thinly bedded turbidites Dawn Houliston BP

Thinly bedded turbidite systems may contain a large proportion of thin, potentially centimetre-scale beds which cannot be accurately resolved by logs or core plugs, yet still contribute to production. The inability to resolve such beds commonly precludes accurate reservoir quality estimation. Facies based rock typing offers a potential solution to understanding the reservoir quality and predicting permeability and flow rates.

Rock typing using open hole log data was utilised as a method of understanding the controls on reservoir quality. A rich core dataset including core descriptions, thin sections and routine and special core analysis allows a good calibration of predicted facies, porosity and permeability from logs to ground truth this technique.

This talk will examine some of the methodologies that can be used to establish different rock type groupings, whether petrophysical or geological, in terms of their reservoir quality. From these groupings different relationships between porosity and permeability can be determined. In particular, this talk will discuss a facies based modelling approach that neatly divides the core porosity and permeability data into groups; is predictable from log data; and relates to geologically predictable facies for inter-well space modelling. This facies based model allows a good correlation between core and log permeability and facies, even in thin beds.

Core porosity vs core permeability Coloured by facies

Conglomerates Thick sands Interbedded Breccia Debrites Siltstones Claystones Limestones

Rock typing for relative permeability Hendrik Rohler OMV

A central goal of conventional petrophysical analysis is the assessment of facies and fluid phase specific reservoir storage and flow capacities. Related workflows set out with core calibrated porosity-permeability-saturation log analysis and end with defining representative relative permeability functions for export into dynamic simulations.

The presentation will go through a workflow applied in recent clastic reservoir IOR projects. Rock typing starts with coarse scale log and fine scale routine core data binning to arrive at a-priori rock classes. This is followed by CT-based homogeneity screening of SCAL plugs to select representative class members for subsequent flooding tests. Multi-stage centrifuge tests with effluent volume logging are run to obtain representative oil and water relative permeability and imbibition capillary pressure functions by means of both, analytical inversion and core flood history matching. In a final refinement step, relative permeability functions are normalized and crosschecked against vintage steady-state core flood mid-saturation range results, leading to a simplified a-posteriori rock class scheme. Reservoir simulator input is then extracted in a straightforward manner.

Good and not so good experiences with the above workflow will be shared, during the talk or over a glass of wine ;-)

Sedimentological analysis and permeability prediction within a heterogeneous carbonate reservoir using borehole images and historical dipmeters Giancarlo Rizzi Task Fronterra Geoscience

Carbonate reservoirs often display considerable heterogeneity ranging from the reservoir to the micro-scale, which can result in significant variations in permeability and hence producibility. within these heterogeneous reservoirs can be fairly constant, but permeabilities may vary by several orders of magnitude for a given porosity value, reflecting changes in the character of the pore system.

An integrated core and borehole image study has been undertaken on the Lower Cretaceous Thamama Group from a field in the UAE, in order to predict lithofacies and permeability distribution within heterogeneous reservoirs.

The borehole images and openhole log data have been calibrated using core and an image log facies scheme established. These image facies have been used to predict lithofacies distribution and porosity-permeability characteristics in uncored wells. The image facies display an overall coarsening-upward trend, reflecting deposition within a shallowing/shoaling-upward storm- influenced carbonate shelf succession. Analysis of image facies stacking patterns also allows the identification of smaller scale parasequences or cycles.

The reservoir is characterised by considerable small-scale permeability heterogeneity, which is related to primary depositional facies and to a lesser extent diagenesis. Individual image facies are comparatively distinct. In particular, thin vuggy bioclastic-rich facies that form high permeability streaks and tight resistive intervals are readily identified from the borehole images. An image rock type (IRT) scheme has been established based on image facies and openhole log response (mainly neutron-density logs), which can be related directly with the core-based petrophysically defined rock type scheme. A permeability ranking has been applied to each image rock type and used to provide an estimate of permeability distribution and heterogeneity within uncored wells. The study was then extended to ‘historical’ 4-arm dipmeter data. A dipmeter facies scheme was established following the image-based facies scheme previously established. Although not providing as much detailed sedimentological information as the modern image logs, it was possible to identify equivalents to most image facies. The dipmeter facies were grouped into a series of dipmeter rock types in order to predict permeability distribution.

This study demonstrates that where borehole images are calibrated with core data meaningful geological interpretations can be made in uncored intervals or wells. It also shows the value of revisiting ‘historical’ data using new processing techniques.

Rock type based poroperm & continuous permeability Samuel Ojo University of Leeds

Porosity from log response such as density provides a continuous representation of pore volume as function of depth in a well, which can be calibrated with core analysis data. Obtaining a continuous log of permeability is not so straightforward as there isn’t yet a means of logging permeability in situ except through laboratory measurement of core samples. It is, however, possible to obtain a depth-continuous permeability estimate by deriving a free regression algorithm known as the poroperm transform function, which defines how the permeability varies as a function of porosity. Such correlations are typically derived empirically from overburden corrected core-derived porosity and permeability data. If core analysis data isn’t available porosity-permeability transforms need to be obtained from other sources. General porosity-permeability trends are far too scattered to be of use. However, far tighter porosity-permeability trends can be obtained by use of rock typing to identify suitable analogues.

The following study highlights how rock-tying can be used to improve permeability prediction in a set of tight gas sandstone wells. Scanning electron microscopy (SEM) and quantitative X-ray diffraction (QXRD) data were obtained from >200 tight gas sandstone samples for which porosity and permeability measurements had been conducted. The SEM and QXRD data were used to derive rock-types. Three simple microstructural rock-types were used based on the clay content and distribution: (i) clay-free sample; (ii) grain coating clay dominated samples and (iii) pore-filling clay samples. The microstructural rock types appear to have a strong relationship with the prevailing authigenic clay minerals so are controlled by diagenesis. In particular, SEM analysis reveals samples identified as grain coating are mostly dominated by illite followed by the coexistence of illite and kaolin and lastly by kaolin. Samples identified as pore filling are mostly dominated by kaolin. Samples identified as having a low clay content are mainly composed of quartz.

Samples from each rock-types occupy different but overlapping positions on porosity- permeability cross plots. Exponential functions were fitted to porosity-permeability data for each rock type and then applied to the porosity values from wire-line log data to derive continuous permeability estimates. The log porosity curves-being the independent variables of the respective functions, were validated by core observation to avoid error propagation.

Discriminating a reservoir using different rock typing techniques has resulted in deriving several poroperm functions which were applied to generate different permeability curves from wire- line log data. The disparity observed between versions of predicted permeability curves, even at intervals of a single rock type necessitated quality checking each technique by comparing its predicted permeability curve with the Klinkenberg and stress corrected core permeability range.

The ‘‘Unique Flow Units (UFU)’’ as defined in this study are zones whose core permeabilities stand out and anomalously differ by large extent from the continuous log of permeability that was predicted by the established poroperm function. These zones were identified and accounted for by adjusting the poroperm function to include an arbitrary constant β in the form Κ = (A * 10 β) e (βφ) where β is any whole number in the range :{ 0, 1, 2, 3, 4}. The difficult situation encountered in this study is how to identify and account for this UFU where there are no routine core analysis data. This presentation will address different methods on how that could be solved.

Influence of formation factors on wettability from 1D NMR Federica Raimondi Imperial

Accurate knowledge of dynamic reservoir behaviour is crucial for determining an effective field development plan. Many of the key dynamic reservoir properties are governed by wettability, making it one of the fundamental factors of importance to operators. However, despite being such an important parameter, wettability is not quantitatively well-defined. The three most widely used measurements of wettability – Amott-Harvey, U.S. Bureau of Mines and contact angle – are often unrepresentative and time consuming. Nuclear Magnetic Resonance (NMR) is another approach that has gained popularity in the last decades. Wettability can be derived from NMR using a first principles approach whose downhole application enables its rapid determination.

The utility of NMR for determining wettability from raw T2 distributions is enhanced or constrained by several factors. Five of the most important formation factors considered here are wetting fraction, saturation, viscosity, pore size, and pore shape, studied through experimental design and an analytic model. Wetting fraction and viscosity were found to have negative correlation with the apparent T2, whereas pore size and saturation were positively correlated. Pore shape has little effect on the position and shape of the distribution, but impacts the sensitivity of T2 to the formation factors.

Results elucidate the limits of NMR in resolving wettability both in the lab and downhole for the five key formation factors. The analytic model can be used by operators to determine the reservoir conditions for which running an NMR tool will provide useful information, and whether data acquisition would benefit from stationary measurement. It can also be used to identify the interpretational limits for particular measurements, and to test inversion algorithms, which require a good understanding of the effects of factors on the T2 distributions to help constrain them and reduce error.

Although the main focus has been to examine how formation factors impact NMR wettability measurement, results show that NMR can also be used as an indicator of rock type. Pore size strongly influences the location of the T2 peaks of oil and water – large pores approach bulk relaxation times whereas very small pores relax too fast to be measured. Pore shape is more difficult to measure as it is not manifested in the overall T2 distributions, but rather in their sensitivity to the formation factors. Nonetheless, pore shape can be inferred from sensitivity tests on the NMR model.

Evaluation of variable oil volume and rock types with NMR and dielectric dispersion logs in a carbonate reservoir high deviation drain Carole Reynaud Perenco

We examine the along-hole profiles of oil and water in the highly deviated drain of a light oil carbonate reservoir offshore UK, and analyse the discrepancy between the volumes calculated from LWD resistivity-porosity (Archie) and from magnetic resonance and dielectric dispersion wireline tools conveyed by open hole tractor. We also correlate the petrographic analysis of these bioclastic (ostreiid) grainstones and packstones to the logs responses and rock types.

Following the acquisition of LWD resistivity and porosity logs, additional formation evaluation logs – a combined tool string of NMR, dielectric dispersion, borehole images and formation pressure – was recorded in a single run conveyed by an open hole tractor between the toe and heel of the highly deviated (60-70 deg.) drain.

The water volume profile calculated from resistivity-porosity Archie analysis – the only data type available in earlier wells – is compared to the volumes of capillary bound water from NMR and to the dielectric dispersion water-filled porosity. In a well drilled with oil base mud, we expect that oil filtrate invades the formation, so that the water volume measured by dielectric dispersion represents capillary bound plus clay-bound water volume and should be equal to the bound fluid volume measured by a NMR tool. Differences between these water volumes represent either formation evaluation anomalies or the presence of free (moveable) water.

Guided by the petrographic analysis of core samples from the same formation and the textural information from the NMR and dielectric dispersion logs, 3 main rock types are identified, to be propagated onto the other field wells with conventional porosity, resistivity, and GR logs.

The evaluation of LWD resistivity-porosity logs provides moderate and almost constant water volumes along the length of the drain, seemingly independent of total porosity variations. In contrast, the water volumes from NMR and dielectric dispersion are more variable, correlating well with total porosity and with the laminated features observed on the borehole images. We also observe that the NMR bound fluid water volume matches the dielectric water volume, and that they are both larger than the resistivity-porosity Archie water volumes.

We propose that the NMR and dielectric water volumes are correct and correspond to variations in reservoir properties in the different rock types, that the more accurate hydrocarbon volume profile is provided by the difference between total porosity and NMR bound fluid volume or dielectric porosity, and that water is at irreducible saturation along the whole drain section.

Although the NMR and dielectric dispersion logs have been used before to resolve carbonate formation evaluation problems, they have rarely been used in highly deviated drains and it is likely that the tractor conveyance and tools combination is unique to this project. Bioclastic (ostreiid) grainstone and packstone reservoirs are also rare, as is the correlation of petrographic analysis to NMR and dielectric dispersion logs in this environment.